SOTAVerified

Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1761–1770 of 1963 papers

TitleStatusHype
On Bochner's and Polya's Characterizations of Positive-Definite Kernels and the Respective Random Feature Maps—0
Learning Scalable Deep Kernels with Recurrent StructureCode0
Gaussian Process Kernels for Popular State-Space Time Series Models—0
Parallelizable sparse inverse formulation Gaussian processes (SpInGP)—0
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation—0
Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification using Markov Random Fields—0
Mean-Field Variational Inference for Gradient Matching with Gaussian Processes—0
Spatio-temporal Gaussian processes modeling of dynamical systems in systems biology—0
Random Feature Expansions for Deep Gaussian ProcessesCode0
Model Selection for Gaussian Process Regression by Approximation Set Coding—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified